MRI Evaluation and Characterization of Ovarian Lesions Based on Ovarian-Adnexal Reporting and Data System MRI.

Background Managing ovarian lesions requires differentiating between benign and malignant cases. The development of a multiparametric MRI approach combining anatomical and functional criteria has led to the creation of the Ovarian-Adnexal Reporting and Data System (O-RADS) MRI scoring system, which enhances diagnostic accuracy. Objectives To study ovarian lesions and their characteristics, along with their risk stratification based on MRI O-RADS. Methods  A prospective study used the O-RADS MRI criteria to categorize ovarian lesions. Clinical findings and MRI results were compared with histopathological outcomes to assess diagnostic accuracy. Results We identified abdominal pain as the most prevalent clinical finding among our cases (64, 91.43%), followed by a lump in the abdomen (33, 47.5%), dysmenorrhea (33, 47.5%), bleeding per vaginal (15, 21.43%), and weight loss (11, 15.71%). A total of 80 ovarian lesions were examined and characterized on the basis of the O-RADS MRI risk stratification system. Among the 80 ovarian lesions, 54 were histopathologically confirmed ovarian lesions (39 (72.22%) were benign, and 15 (27.77%) were malignant). The most common benign lesions were ovarian serous cystadenoma (28.20%) and ovarian mucinous cystadenoma (20.51%), while the most common malignant lesions were serous carcinoma (33.33%) and mucinous carcinoma (20%). Using the O-RADS MRI scoring system, we categorized six lesions (7.5%) as O-RADS 1 (all benign), 34 lesions (42.50%) as O-RADS 2 (32 benign and 2 malignant), 24 lesions (30%) as O-RADS 3 (23 benign and 1 malignant), seven lesions (8.75%) as O-RADS 4 (four benign and three malignant), and nine lesions (11.25%) as O-RADS 5 (all malignant). Our findings revealed significant differences in the size of lesions, the presence of thick septa, high T2-weighted signal intensity within solid tissue, and patterns of solid component enhancement and wall irregularity between malignant and benign lesions. The MRI cut-off score of ≥4 for malignancy demonstrated a sensitivity of 94.59%, a specificity of 97.5%, an accuracy of 97.62%, a positive predictive value of 94.5%, and a negative predictive value of 97.5%. The positive likelihood ratio was 32.7, while the negative likelihood ratio was 0.025. These results affirm the high diagnostic accuracy of the O-RADS MRI scoring system in distinguishing benign from malignant ovarian lesions. Conclusion The O-RADS MRI score is a highly accurate tool for differentiating between benign and malignant ovarian lesions. Its application can significantly enhance the management and treatment outcomes for patients with adnexal masses. The study confirms the scoring system's high sensitivity, specificity, and overall diagnostic accuracy.

Clinical value of ACR O-RADS combined with CA125 in the risk stratification of adnexal masses.

Purpose: To develop a combined diagnostic model integrating the subclassification of the 2022 version of the American College of Radiology (ACR) Ovarian-Adnexal Reporting and Data System (O-RADS) with carbohydrate antigen 125 (CA125) and to validate whether the combined model can offer superior diagnostic efficacy than O-RADS alone in assessing adnexal malignancy risk. Methods: A retrospective analysis was performed on 593 patients with adnexal masses (AMs), and the pathological and clinical data were included. According to the large differences in malignancy risk indices for different image features in O-RADS category 4, the lesions were categorized into groups A and B. A new diagnostic criterion was developed. Lesions identified as category 1, 2, 3, or 4A with a CA125 level below 35 U/ml were classified as benign. Lesions identified as category 4A with a CA125 level more than or equal to 35 U/ml and lesions with a category of 4B and 5 were classified as malignant. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and area under the curve (AUC) of O-RADS (v2022), CA125, and the combined model in the diagnosis of AMs were calculated and compared. Results: The sensitivity, specificity, PPV, NPV, accuracy, and AUCs of the combined model were 92.4%, 96.5%, 80.2%, 98.8%, 94.1%, and 0.945, respectively. The specificity, PPV, accuracy, and AUC of the combined model were significantly higher than those of O-RADS alone (all P < 0.01). In addition, both models had acceptable sensitivity and NPV, but there were no significant differences among them (P > 0.05). Conclusion: The combined model integrating O-RADS subclassification with CA125 could improve the specificity and PPV in diagnosing malignant AMs. It could be a valuable tool in the clinical application of risk stratification of AMs.

Diagnostic performances of the Ovarian Adnexal Reporting and Data System, the Risk of Ovarian Malignancy Algorithm, and the Copenhagen Index in the preoperative prediction of ovarian cancer: a prospective cohort study.

OBJECTIVE: This study aimed to assess the diagnostic performance of the Risk of Ovarian Malignancy Algorithm (ROMA), Copenhagen Index (CPH-I), and Ovarian Adnexal Reporting and Data System (O-RADS) for the preoperative prediction of ovarian cancer (OC). METHODS: A prospective cohort study was conducted on 462 patients diagnosed with ovarian tumors admitted to the Departments of Obstetrics and Gynecology, Hue University of Medicine and Pharmacy Hospital, and Hue Central Hospital from May 2020 to December 2022. ROMA and CPH-I were calculated using cancer antigen 125 (CA125), human epididymal protein 4 (HE4) levels, and patient characteristics (age and menopausal status). O-RADS criteria were applied to describe ovarian tumor characteristics from ultrasound findings. Compared with histopathological results, the predictive values of ROMA, CPH-I, and O-RADS alone or in combination with CA125/HE4 for OC were calculated. RESULTS: Among 462 patients, 381 had benign tumors, 11 had borderline tumors, and 50 had OC. At optimal cut-off points, ROMA's and CPH-I's areas under the curves (AUCs) were 0.880 (95% confidence interval [CI]=0.846-0.909) and 0.890 (95% CI=0.857-0.918), respectively, and ROMA and CPH-I sensitivities/specificities (Se/Sp) were 68.85%/95.01% and 77.05%/91.08%, respectively. O-RADS ≥3 yielded an AUCs of 0.949 (95% CI=0.924-0.968), with Se/Sp of 88.52%/88.98% (p<0.001). Combining O-RADS with CA125 demonstrated the highest predictive value, with AUCs of 0.969 (95% CI=0.949-0.983) and Se/Sp of 98.36%/86.09% (p<0.001). CONCLUSION: The ROMA, CPH-I, O-RADS, O-RADS + CA125, and O-RADS + HE4 models demonstrated good predictive values for OC; the combination of O-RADS and CA125 yielded the highest values.

Reliability, reproducibility, and potential pitfalls of the O-RADS scoring with non-dynamic MRI.

BACKGROUND: The O-RADS scoring has been proposed to standardize the reporting of adnexal lesions using magnetic resonance imaging (MRI). PURPOSE: To assess intra- and inter-observer agreement of the O-RADS scoring using non-dynamic MRI and its agreement with pathologic diagnosis, and to provide the pitfalls in the scoring based on discordant ratings. MATERIAL AND METHODS: Adnexal lesions that were diagnosed using non-dynamic MRI at two centers were scored using O-RADS. Intra- and inter-observer agreements were assessed using kappa statistics. Cross-tabulations were made for intra- and inter-observer ratings and for O-RADS scores and pathological findings. RESULTS: Intra- and inter-observer agreements were assessed for 404 lesions in 339 patients who were admitted to center 1. Intra-observer agreement was almost perfect (97.8%, kappa = 0.963) and inter-observer agreement was substantial (83.2%, kappa = 0.730). The combined data from center 1 and center 2 included 496 patients; of them, 295 (59.5%) were operated. There was no borderline or malignant pathology for the lesions with O-RADS 1 or 2. Of those with an O-RADS score of 3, 3 (4.1%) lesions were borderline and none were malignant. The O-RADS scoring in discriminating borderline/malignant lesions from benign lesions was outstanding (area under the ROC curve 0.950, 95% CI = 0.923-0.971). Sensitivity, specificity, positive, and negative predictive values of O-RADS 4/5 lesions for borderline/malignant lesions were 96.2%, 87.1%, 72.8%, and 98.4%, respectively. CONCLUSION: The O-RADS scoring using non-dynamic MRI is a reproducible method and has good discrimination for borderline/malignant lesions. Potential factors that may lead to discordant ratings are provided here.

Primarian fallopian tube carcinoma: Clinical and radiological keys for diagnosis.

Primary fallopian tube carcinoma (PFTC) is seldom diagnosed preoperatively and is often mistaken for epithelial ovarian carcinoma (EOC). This report details a case of primary high-grade serous carcinoma (HGSC) of the fallopian tube, highlighting radiological and clinical indicators to aid in accurate diagnosis and avoid misdiagnosis. A 46-year-old premenopausal woman presented with symptoms and a transvaginal ultrasound (TVUS) indicating a malignant ovarian tumor. Further imaging with CT and MRI revealed a solid-cystic mass suggestive of a fallopian tube tumor rather than an ovarian origin. Oncological surgery confirmed the presence of a high-grade serous carcinoma in the fallopian tube. This case underscores the diagnostic challenges of PFTC and the superior sensitivity and specificity of MRI over CT and US in distinguishing adnexal lesions. Key MRI features such as the sausage-shaped mass and associated hematosalpinx were crucial in differentiating PFTC from EOC. The report emphasizes the importance of considering PFTC in differential diagnoses of adnexal masses to ensure accurate preoperative identification.

Analysis of Diagnostic Efficacy of the International Ovarian Tumor Analysis ADNEX Model and the ACR O-RADS US (Ovarian-Adnexal Reporting and Data System) for Benign and Malignant Ovarian Tumors: A Retrospective Study in a Tumor Center in Northeast China.

This study is to analyze and compare the diagnostic efficacy of the ADNEX model and O-RADS in Northeast China for benign and malignant ovarian-adnexal tumors. From July 2020 to February 2022, ultrasound images of 312 ovarian-adnexal masses included in the study were analyzed retrospectively, and the properties of these masses were identified using the ADNEX model and O-RADS. The diagnostic efficiency of the ADNEX model and O-RADS was analyzed using a ROC curve, and the capacities of the two models in differentiating benign and malignant ovarian masses at the optimum cutoff value were compared, as well as the consistency of their diagnosis results was evaluated. The study included 312 ovarian-adnexal masses, including 145 malignant masses and 167 benign masses from 287 patients with an average age of (46.8 ± 11.3) years. The AUC of the ADNEX model was 0.974, and the optimum cutoff value was the risk value > 24.2%, with the corresponding sensitivity and specificity being 97.93 and 86.83, respectively. The AUC of the O-RADS was 0.956, and the optimum cutoff value was > O-RADS 3, with the corresponding sensitivity and specificity being 97.24 and 85.03, respectively. The AUCs of the two models were 0.924 and 0.911 at the optimum cutoff values, with no statistical differences between them (P = 0.284). Consistency analysis: the kappa values of the two models for the determination and pathological results of masses were 0.840 and 0.815, respectively, and that for the diagnostic outcomes was 0.910. Both the ADNEX model and O-RADS had good diagnostic performance in people from Northeast China. Their diagnostic capabilities were similar, and diagnostic results were highly consistent at the optimum cutoff values.

Evaluating Large Language Models for Automated Reporting and Data Systems Categorization: Cross-Sectional Study.

BACKGROUND: Large language models show promise for improving radiology workflows, but their performance on structured radiological tasks such as Reporting and Data Systems (RADS) categorization remains unexplored. OBJECTIVE: This study aims to evaluate 3 large language model chatbots-Claude-2, GPT-3.5, and GPT-4-on assigning RADS categories to radiology reports and assess the impact of different prompting strategies. METHODS: This cross-sectional study compared 3 chatbots using 30 radiology reports (10 per RADS criteria), using a 3-level prompting strategy: zero-shot, few-shot, and guideline PDF-informed prompts. The cases were grounded in Liver Imaging Reporting & Data System (LI-RADS) version 2018, Lung CT (computed tomography) Screening Reporting & Data System (Lung-RADS) version 2022, and Ovarian-Adnexal Reporting & Data System (O-RADS) magnetic resonance imaging, meticulously prepared by board-certified radiologists. Each report underwent 6 assessments. Two blinded reviewers assessed the chatbots' response at patient-level RADS categorization and overall ratings. The agreement across repetitions was assessed using Fleiss κ. RESULTS: Claude-2 achieved the highest accuracy in overall ratings with few-shot prompts and guideline PDFs (prompt-2), attaining 57% (17/30) average accuracy over 6 runs and 50% (15/30) accuracy with k-pass voting. Without prompt engineering, all chatbots performed poorly. The introduction of a structured exemplar prompt (prompt-1) increased the accuracy of overall ratings for all chatbots. Providing prompt-2 further improved Claude-2's performance, an enhancement not replicated by GPT-4. The interrun agreement was substantial for Claude-2 (k=0.66 for overall rating and k=0.69 for RADS categorization), fair for GPT-4 (k=0.39 for both), and fair for GPT-3.5 (k=0.21 for overall rating and k=0.39 for RADS categorization). All chatbots showed significantly higher accuracy with LI-RADS version 2018 than with Lung-RADS version 2022 and O-RADS (P<.05); with prompt-2, Claude-2 achieved the highest overall rating accuracy of 75% (45/60) in LI-RADS version 2018. CONCLUSIONS: When equipped with structured prompts and guideline PDFs, Claude-2 demonstrated potential in assigning RADS categories to radiology cases according to established criteria such as LI-RADS version 2018. However, the current generation of chatbots lags in accurately categorizing cases based on more recent RADS criteria.

Apparent diffusion coefficient analysis of solid tissue helps distinguish borderline from invasive malignant adnexal masses rated O-RADS MRI 4.

PURPOSE: The purpose of this study was to evaluate the contribution of apparent diffusion coefficient (ADC) analysis of the solid tissue of adnexal masses to optimize tumor characterization and possibly refine the risk stratification of the O-RADS MRI 4 category. MATERIALS AND METHODS: The EURAD cohort was retrospectively analyzed to select all patients with an adnexal mass with solid tissue and feasible ADC measurements. Two radiologists independently measured the ADC values of solid tissue, excluding necrotic areas, surrounding structures, and magnetic susceptibility artifacts. Significant differences in diffusion quantitative parameters in the overall population and according to the morphological aspect of solid tissue were analyzed to identify its impact on ADC reliability. Receiver operating characteristics curve (ROC) was used to determine the optimum cutoff of the ADC for distinguishing invasive from non-invasive tumors in the O-RADS MRI score 4 population. RESULTS: The final study population included 180 women with a mean age of 57 ± 15.5 (standard deviation) years; age range: 19-95 years) with 93 benign, 23 borderline, and 137 malignant masses. The median ADC values of solid tissue was greater in borderline masses (1.310 × 10-3 mm2/s (Q1, Q3: 1.152, 1.560 × 10-3 mm2/s) than in benign masses (1.035 × 10-3 mm2/s; Q1, Q3: 0.900, 1.560 × 10-3 mm2/s) (P= 0.002) and in benign tumors compared by comparison with invasive masses (0.850 × 10-3 mm2/s; Q1, Q3: 0.750, 0.990 × 10-3 mm2/s) (P < 0.001). Solid tissue corresponded to irregular septa or papillary projection in 18.6% (47/253), to a mural nodule or a mixed mass in 46.2% (117/253), and to a purely solid mass in 35.2% (89/253) of adnexal masses. In mixed masses or masses with mural nodule subgroup, invasive masses had a significantly lower ADC (0.830 × 10-3 mm2/s (Q1, Q3: 0.738, 0.960) than borderline (1.385; Q1, Q3: 1.300, 1.930) (P= 0.0012) and benign masses (P= 0.04). An ADC cutoff of 1.08 × 10-3 mm2/s yielded 71.4% sensitivity and 100% specificity for identifying invasive lesions in the mixed or mural nodule subgroup with an AUC of 0.92 (95% confidence interval: 0.76-0.99). CONCLUSION: ADC analysis of solid tissue of adnexal masses could help distinguish invasive masses within the O-RADS MRI 4 category, especially in mixed masses or those with mural nodule.

Ultrasound image-based nomogram combining clinical, radiomics, and deep transfer learning features for automatic classification of ovarian masses according to O-RADS.

Background: Accurate and rapid discrimination between benign and malignant ovarian masses is crucial for optimal patient management. This study aimed to establish an ultrasound image-based nomogram combining clinical, radiomics, and deep transfer learning features to automatically classify the ovarian masses into low risk and intermediate-high risk of malignancy lesions according to the Ovarian- Adnexal Reporting and Data System (O-RADS). Methods: The ultrasound images of 1,080 patients with 1,080 ovarian masses were included. The training cohort consisting of 683 patients was collected at the South China Hospital of Shenzhen University, and the test cohort consisting of 397 patients was collected at the Shenzhen University General Hospital. The workflow included image segmentation, feature extraction, feature selection, and model construction. Results: The pre-trained Resnet-101 model achieved the best performance. Among the different mono-modal features and fusion feature models, nomogram achieved the highest level of diagnostic performance (AUC: 0.930, accuracy: 84.9%, sensitivity: 93.5%, specificity: 81.7%, PPV: 65.4%, NPV: 97.1%, precision: 65.4%). The diagnostic indices of the nomogram were higher than those of junior radiologists, and the diagnostic indices of junior radiologists significantly improved with the assistance of the model. The calibration curves showed good agreement between the prediction of nomogram and actual classification of ovarian masses. The decision curve analysis showed that the nomogram was clinically useful. Conclusion: This model exhibited a satisfactory diagnostic performance compared to junior radiologists. It has the potential to improve the level of expertise of junior radiologists and provide a fast and effective method for ovarian cancer screening.

Comparison of O-RADS with the ADNEX model and IOTA SR for risk stratification of adnexal lesions: a systematic review and meta-analysis.

Purpose: This study aims to systematically compare the diagnostic performance of the Ovarian-Adnexal Reporting and Data System with the International Ovarian Tumor Analysis Simple Rules and the Assessment of Different NEoplasias in the adneXa model for risk stratification of ovarian cancer and adnexal masses. Methods: A literature search of online databases for relevant studies up to July 2023 was conducted by two independent reviewers. The summary estimates were pooled with the hierarchical summary receiver-operating characteristic model. The quality of the included studies was assessed with the Quality Assessment of Diagnostic Accuracy Studies-2 and the Quality Assessment of Diagnostic Accuracy Studies-Comparative Tool. Metaregression and subgroup analyses were performed to explore the impact of varying clinical settings. Results: A total of 13 studies met the inclusion criteria. The pooled sensitivity and specificity for eight head-to-head studies between the Ovarian-Adnexal Reporting and Data System and the Assessment of Different NEoplasias in the adneXa model were 0.96 (95% CI 0.92-0.98) and 0.82 (95% CI 0.71-0.90) vs. 0.94 (95% CI 0.91-0.95) and 0.83 (95% CI 0.77-0.88), respectively, and for seven head-to-head studies between the Ovarian-Adnexal Reporting and Data System and the International Ovarian Tumor Analysis Simple Rules, the pooled sensitivity and specificity were 0.95 (95% CI 0.93-0.97) and 0.75 (95% CI 0.62-0.85) vs. 0.91 (95% CI 0.82-0.96) and 0.86 (95% CI 0.76-0.93), respectively. No significant differences were found between the Ovarian-Adnexal Reporting and Data System and the Assessment of Different NEoplasias in the adneXa model as well as the International Ovarian Tumor Analysis Simple Rules in terms of sensitivity (P = 0.57 and P = 0.21) and specificity (P = 0.87 and P = 0.12). Substantial heterogeneity was observed among the studies for all three guidelines. Conclusion: All three guidelines demonstrated high diagnostic performance, and no significant differences in terms of sensitivity or specificity were observed between the three guidelines.

A Case of Solid-Appearing Struma Ovarii: Pitfall in the Assessment Using Ovarian-Adnexal Reporting and Data System Magnetic Resonance Imaging Score.

Struma ovarii is a monodermal teratoma characterized by the presence of >50% thyroid tissue. It is mostly benign; therefore, preoperative diagnosis is important. It usually manifests as a multilocular cystic mass but rarely as a predominantly solid mass. On magnetic resonance imaging (MRI), solid-appearing struma ovarii showed early signal intensity enhancement on dynamic gadolinium-enhanced T1-weighted images, which histopathologically indicates the presence of thyroid tissue with abundant blood vessels. The Ovarian-Adnexal Reporting and Data System (O-RADS) MRI score is a validated classification worldwide for characterizing adnexal lesions. Based on the morphology, signal intensity, and enhancement of any solid tissue on the MRI, the scoring system can be used to classify adnexal lesions into five categories from score one (no adnexal mass) to score five (high risk of malignancy). An adnexal solid mass with a higher signal intensity than that of the myometrium 30-40 seconds after gadolinium (Gd) injection on non-dynamic contrast-enhanced (non-DCE) MRI was assigned a score of 5 (high risk of malignancy).  We present a case of solid-appearing struma ovarii with a higher signal intensity than that of the myometrium 30 seconds after Gd injection on non-DCE MRI, and it was classified as score five preoperatively. Therefore, a total abdominal hysterectomy with bilateral salpingo-oophorectomy was performed despite the presence of a benign ovarian mass. When an adnexal mass with a higher signal intensity than that of the myometrium 30-40 seconds after Gd injection on non-DCE MRI is encountered, struma ovarii should be included in the differential diagnosis, despite the O-RADS MRI score of five and management of the situation should be discussed.

Development and validation of an ultrasound-based deep learning radiomics nomogram for predicting the malignant risk of ovarian tumours.

BACKGROUND: The timely identification and management of ovarian cancer are critical determinants of patient prognosis. In this study, we developed and validated a deep learning radiomics nomogram (DLR_Nomogram) based on ultrasound (US) imaging to accurately predict the malignant risk of ovarian tumours and compared the diagnostic performance of the DLR_Nomogram to that of the ovarian-adnexal reporting and data system (O-RADS). METHODS: This study encompasses two research tasks. Patients were randomly divided into training and testing sets in an 8:2 ratio for both tasks. In task 1, we assessed the malignancy risk of 849 patients with ovarian tumours. In task 2, we evaluated the malignancy risk of 391 patients with O-RADS 4 and O-RADS 5 ovarian neoplasms. Three models were developed and validated to predict the risk of malignancy in ovarian tumours. The predicted outcomes of the models for each sample were merged to form a new feature set that was utilised as an input for the logistic regression (LR) model for constructing a combined model, visualised as the DLR_Nomogram. Then, the diagnostic performance of these models was evaluated by the receiver operating characteristic curve (ROC). RESULTS: The DLR_Nomogram demonstrated superior predictive performance in predicting the malignant risk of ovarian tumours, as evidenced by area under the ROC curve (AUC) values of 0.985 and 0.928 for the training and testing sets of task 1, respectively. The AUC value of its testing set was lower than that of the O-RADS; however, the difference was not statistically significant. The DLR_Nomogram exhibited the highest AUC values of 0.955 and 0.869 in the training and testing sets of task 2, respectively. The DLR_Nomogram showed satisfactory fitting performance for both tasks in Hosmer-Lemeshow testing. Decision curve analysis demonstrated that the DLR_Nomogram yielded greater net clinical benefits for predicting malignant ovarian tumours within a specific range of threshold values. CONCLUSIONS: The US-based DLR_Nomogram has shown the capability to accurately predict the malignant risk of ovarian tumours, exhibiting a predictive efficacy comparable to that of O-RADS.

O-RADS MRI risk stratification system: pearls and pitfalls.

In 2021, the American College of Radiology (ACR) Ovarian-Adnexal Reporting and Data System (O-RADS) MRI Committee developed a risk stratification system and lexicon for assessing adnexal lesions using MRI. Like the BI-RADS classification, O-RADS MRI provides a standardized language for communication between radiologists and clinicians. It is essential for radiologists to be familiar with the O-RADS algorithmic approach to avoid misclassifications. Training, like that offered by International Ovarian Tumor Analysis (IOTA), is essential to ensure accurate and consistent application of the O-RADS MRI system. Tools such as the O-RADS MRI calculator aim to ensure an algorithmic approach. This review highlights the key teaching points, pearls, and pitfalls when using the O-RADS MRI risk stratification system.Critical relevance statement This article highlights the pearls and pitfalls of using the O-RADS MRI scoring system in clinical practice.Key points• Solid tissue is described as displaying post- contrast enhancement.• Endosalpingeal folds, fimbriated end of the tube, smooth wall, or septa are not solid tissue.• Low-risk TIC has no shoulder or plateau. An intermediate-risk TIC has a shoulder and plateau, though the shoulder is less steep compared to outer myometrium.

O-RADS MRI to classify adnexal tumors: from clinical problem to daily use.

Eighteen to 35% of adnexal masses remain non-classified following ultrasonography, leading to unnecessary surgeries and inappropriate management. This finding led to the conclusion that ultrasonography was insufficient to accurately assess adnexal masses and that a standardized MRI criteria could improve these patients' management. The aim of this work is to present the different steps from the identification of the clinical issue to the daily use of a score and its inclusion in the latest international guidelines. The different steps were the following: (1) preliminary work to formalize the issue, (2) physiopathological analysis and finding dynamic parameters relevant to increase MRI performances, (3) construction and internal validation of a score to predict the nature of the lesion, (4) external multicentric validation (the EURAD study) of the score named O-RADS MRI, and (5) communication and education work to spread its use and inclusion in guidelines. Future steps will include studies at patients' levels and a cost-efficiency analysis. Critical relevance statement We present translating radiological research into a clinical application based on a step-by-step structured and systematic approach methodology to validate MR imaging for the characterization of adnexal mass with the ultimate step of incorporation in the latest worldwide guidelines of the O-RADS MRI reporting system that allows to distinguish benign from malignant ovarian masses with a sensitivity and specificity higher than 90%. Key points • The initial diagnostic test accuracy studies show the limitation of a preoperative assessment of adnexal masses using solely ultrasonography.• The technical developments (DCE/DWI) were investigated with the value of dynamic MRI to accurately predict the nature of benign or malignant lesions to improve management.• The first developing score named ADNEX MR Score was constructed using multiple easily assessed criteria on MRI to classify indeterminate adnexal lesions following ultrasonography.• The multicentric adnexal study externally validated the score creating the O-RADS MR score and leading to its inclusion for daily use in international guidelines.

Bridging Communication Gaps Between Radiologists, Referring Physicians, and Patients Through Standardized Structured Cancer Imaging Reporting: The Experience with Female Pelvic MRI Assessment Using O-RADS and a Simulated Cohort Patient Group.

RATIONALE AND OBJECTIVES: This study aimed to evaluate whether implementing structured reporting based on Ovarian-Adnexal Reporting and Data System (O-RADS) magnetic resonance imaging (MRI) in women with sonographically indeterminate adnexal masses improves communication between radiologists, referrers, and patients/caregivers and enhances diagnostic performance for determining adnexal malignancy. MATERIALS AND METHODS: We retrospectively analyzed prospectively issued MRI reports in 2019-2022 performed for characterizing adnexal masses before and after implementing O-RADS MRI; 56 patients/caregivers and nine gynecologic oncologists ("referrers") were surveyed about report interpretability/clarity/satisfaction; responses for pre- and post-implementation reports were compared using Fisher's exact and Chi-squared tests. Diagnostic performance was assessed using receiver operating characteristic curves. RESULTS: A total of 123 reports from before and 119 reports from after O-RADS MRI implementation were included. Survey response rates were 35.7% (20/56) for patients/caregivers and 66.7% (6/9) for referrers. For patients/caregivers, O-RADS MRI reports were clearer (p < 0.001) and more satisfactory (p < 0.001) than unstructured reports, but interpretability did not differ significantly (p = 0.14), as 28.0% (28/100) of postimplementation and 38.0% (38/100) of preimplementation reports were considered difficult to interpret. For referrers, O-RADS MRI reports were clearer, more satisfactory, and easier to interpret (p < 0.001); only 1.3% (1/77) were considered difficult to interpret. For differentiating benign from malignant adnexal lesions, O-RADS MRI showed area under the curve of 0.92 (95% confidence interval [CI], 0.85-0.99), sensitivity of 0.81 (95% CI, 0.58-0.95), and specificity of 0.91 (95% CI, 0.83-0.96). Diagnostic performance of reports before implementation could not be calculated due to many different phrases used to describe the likelihood of malignancy. CONCLUSION: Implementing standardized structured reporting using O-RADS MRI for characterizing adnexal masses improved clarity and satisfaction for patients/caregivers and referrers. Interpretability improved for referrers but remained limited for patients/caregivers.

O-RADS MRI SCORE: An Essential First-Step Tool for the Characterization of Adnexal Masses.

The ovarian-adnexal reporting and data system on magnetic resonance imaging (O-RADS MRI) score is now a well-established tool to characterize pelvic gynecological masses based on their likelihood of malignancy. The main added value of O-RADS MRI over O-RADS US is to correctly reclassify lesions that were considered suspicious on US as benign on MRI. The crucial issue when characterizing an adnexal mass is to determine the presence/absence of solid tissue and thus need to perform gadolinium injection. O-RADS MR score was built on a multivariate analysis and must be applied as a step-by-step analysis: 1) Is the mass an adnexal mass? 2) Is there an associated peritoneal carcinomatosis? 3) Is there any significant amount of fatty content? 4) Is there any wall enhancement? 5) Is there any internal enhancement? 6) When an internal enhancement is detected, does the internal enhancement correspond to solid tissue or not? 7) Is the solid tissue malignant? With its high value to distinguish benign from malignant adnexal masses and its high reproducibility, the O-RADS MRI score could be a valuable tool for timely referral of a patient to an expert center for the treatment of ovarian cancers. Finally, to make a precise diagnosis allowing optimal personalized treatment, the radiologist in gynecological imaging will combine the O-RADS MRI score with many other clinical, biological, and other MR criteria to suggest a pathological hypothesis. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY STAGE: 3.

Comparison of International Ovarian Tumor Analysis ADNEX model and Ovarian-Adnexal Reporting and Data System with final histological diagnosis in adnexal masses: a retrospective study.

OBJECTIVE: The International ovarian tumor analysis (IOTA)-Assessment of Different NEoplasias in the adneXa (ADNEX) model and the ovarian-adnexal reporting and data system (O-RADS) were developed to improve the diagnostic accuracy of adnexal masses in the preoperative period. This study aimed to evaluate the predictive values of both models in patients who underwent surgery for an adnexal mass at our hospital, based on the final pathological results. METHODS: This study included patients who underwent surgery for adnexal masses at our hospital between 2019 and 2021 and met the inclusion criteria. The IOTA ADNEX model and O-RADS scores were calculated preoperatively. RESULTS: Of the 413 patients, 295 were diagnosed with benign tumors and 118 were diagnosed with malignant tumors. The mean cancer antigen 125 (CA-125) levels for patients diagnosed with benign and malignant were 15.2 unit/mL and 72.5 unit/mL, respectively. According to the receiver operator characteristic analysis for serum CA-125 in postmenopausal and premenopausal patients, the cutoff value of 34.8 unit/mL had a sensitivity of 70.8% and specificity of 83.8% and 180.5 unit/mL had a sensitivity of 32.1% and a specificity of 92.7%, respectively (P<0.001). The sensitivity and specificity values of the IOTA ADNEX model and O-RADS were found as 78.8-48.3% and 97.9-93.5% respectively (P<0.001). There was moderate agreement between the IOTA ADNEX model and O-RADS (Kappa=0.53). CONCLUSION: The IOTA ADNEX model has a similar specificity to the O-RADS in malignancy risk assessment, but the sensitivity of the IOTA ADNEX model is higher than that of the O-RADS. The IOTA-ADNEX model can help avoid unnecessary surgeries.

Application of O-RADS US combined with MV-Flow to diagnose ovarian-adnexal tumors.

PURPOSE: This study aimed to explore the application of Ovarian-Adnexal Reporting and Data System Ultrasound (O-RADS US) combined with MV-Flow (Samsung Medison Co., Ltd.) to diagnose ovarian-adnexal masses. METHODS: A total of 112 ovarian-adnexal masses (81 benign and 31 malignant) from 105 consecutive patients were analyzed. The O-RADS US and vascular index from MV-Flow (VIMV) were measured and compared with the reference standard. O-RADS US and MV-Flow were tested for consistency. RESULTS: Receiver operating characteristic curves were drawn for O-RADS US, MV-Flow, and their combination. The combined methods had the largest area under the curve (0.955), followed by O-RADS US (0.929) and MV-Flow (0.923). A mass was considered malignant when the O-RADS US classification was 5 and VIMV was ≥7.15. With this definition, MV-Flow had the highest sensitivity (87.10%), with consistent findings for the combined diagnostic methods and O-RADS US (83.87%). The specificity of the combined diagnostic methods (93.83%) was higher than that of MV-Flow (91.36%). O-RADS US had the lowest specificity (90.12%). The combined diagnostic methods had the highest coincidence rate (91.07%), and MV-Flow (90.18%) had a significantly higher coincidence rate than O-RADS US (88.39%). Both O-RADS US and MV-Flow showed good consistency among different physicians (former kappa, 0.974; latter intraclass correlation coefficient [ICC], 0.986). MV-Flow had a high consistency for the same physician (ICC, 1). CONCLUSION: O-RADS US and MV-Flow exhibited good diagnostic efficacy, and their combined diagnostic efficacy was higher than that of each individually. O-RADS US and MV-Flow can improve the diagnosis of benign and malignant ovarian-adnexal masses.

Prediction model of adnexal masses with complex ultrasound morphology.

Background: Based on the ovarian-adnexal reporting and data system (O-RADS), we constructed a nomogram model to predict the malignancy potential of adnexal masses with sophisticated ultrasound morphology. Methods: In a multicenter retrospective study, a total of 430 subjects with masses were collected in the adnexal region through an electronic medical record system at the Fourth Hospital of Harbin Medical University during the period of January 2019-April 2023. A total of 157 subjects were included in the exception validation cohort from Harbin Medical University Tumor Hospital. The pathological tumor findings were invoked as the gold standard to classify the subjects into benign and malignant groups. All patients were randomly allocated to the validation set and training set in a ratio of 7:3. A stepwise regression analysis was utilized for filtering variables. Logistic regression was conducted to construct a nomogram prediction model, which was further validated in the training set. The forest plot, C-index, calibration curve, and clinical decision curve were utilized to verify the model and assess its accuracy and validity, which were further compared with existing adnexal lesion models (O-RADS US) and assessments of different types of neoplasia in the adnexa (ADNEX). Results: Four predictors as independent risk factors for malignancy were followed in the preparation of the diagnostic model: O-RADS classification, HE4 level, acoustic shadow, and protrusion blood flow score (all p < 0.05). The model showed moderate predictive power in the training set with a C-index of 0.959 (95%CI: 0.940-0.977), 0.929 (95%CI: 0.884-0.974) in the validation set, and 0.892 (95%CI: 0.843-0.940) in the external validation set. It showed that the predicted consequences of the nomogram agreed well with the actual results of the calibration curve, and the novel nomogram was clinically beneficial in decision curve analysis. Conclusion: The risk of the nomogram of adnexal masses with complex ultrasound morphology contained four characteristics that showed a suitable predictive ability and provided better risk stratification. Its diagnostic performance significantly exceeded that of the ADNEX model and O-RADS US, and its screening performance was essentially equivalent to that of the ADNEX model and O-RADS US classification.